This document provides the code for the analyses reported in:

Cosme et al. (Preprint) Testing the adolescent social reorientation model using hierarchical growth curve modeling with parcellated fMRI data

This script reproduces the analyses reported in the main manuscript using a different set of parcels categorized as control regions. Specifically, parcels from the original set of control regions that overlapped with the Yeo et al. (2011) 7-network sensorimotor map were categorized as control regions.

load packages

library(tidyverse)
library(knitr)
library(lme4)
library(lmerTest)

define color palettes and labels

pal_self_other = c("#FFA90A", "#247BA0")
pal_social_academic = c("#63647E", "#F25F5C")
pal_wave = c("#693668", "#A74482", "#F84AA7")
pal_label = c("#47A8BD", "#DBC057", "#FF3366")
pal_gender = c("#70c1b3","#247BA0")

parcel_labeller = labeller(label = c('social' = 'social parcels', 'other' = 'control parcels', 'self' = 'self parcels'),
                           domain = c('social' = 'social domain', 'academic' = 'academic domain'),
                           wave = c("t1" = "wave 1", "t2" = "wave 2", "t3" = "wave 3"))

label_df = expand.grid(label = c("social", "self", "other"),
              target = c("self", "other"),
              domain = c("social", "academic"),
              age = 13,
              expected_avg = 1,
              expected_diff = 1)

dcbw = theme_classic() +
  theme(text = element_text(size = 14, family = "Futura Medium", color = "black"),
        panel.background = element_blank(),
        plot.background = element_blank(),
        strip.background = element_blank(),
        strip.text = element_text(size = 14),
        legend.background = element_rect(fill = NA, color = NA),
        axis.line = element_line(color = "black"),
        axis.text = element_text(color = "black"),
        panel.grid.minor = element_blank())

load MRI data

  • exclude participants with >20% volumes with motion artifacts or low quality FX data
  • standardize betas within parcel (across participants and time)
  • recode standardized betas +/- 3 SDs from the mean as NA (0.8% of all observations)
# define parcels
self_parcels = c(5, 17, 28, 47, 52, 66, 116, 147, 152, 156, 184, 198, 207, 225, 249, 292, 309, 354, 380)
social_parcels = c(18, 23, 29, 49, 54, 59, 62, 63, 67, 76, 111, 114, 139, 143, 146, 150, 178, 179, 189, 203, 212, 224, 229, 236, 238, 239, 245, 250, 259, 266, 271, 301, 305, 310, 322, 324, 328, 331, 333, 342, 343, 350, 374, 391)
control_yeo_parcels = c(1, 8, 12, 14, 21, 24, 35, 37, 42, 51, 53, 64, 75, 79, 82, 87, 88, 89, 91, 106, 107, 112, 120, 121, 129, 131, 132, 137, 140, 148, 151, 158, 164, 166, 168, 170, 182, 183, 188, 191, 193, 204, 210, 222, 226, 228, 231, 242, 243, 256, 260, 264, 265, 280, 285, 287, 288, 291, 299, 320, 325, 327, 338, 339, 340, 341, 353, 357, 365, 366, 369, 370, 372, 376, 386, 387, 390)

# mri exclusions
mri_exclusions = c('s002_t1', 's004_t1', 's008_t1', 's011_t1', 's017_t1', 
                   's026_t1', 's033_t2', 's034_t1', 's041_t1', 's044_t1', 
                   's047_t1', 's051_t1', 's054_t1', 's057_t1', 's059_t1', 
                   's061_t1', 's063_t1', 's070_t2', 's074_t1', 's074_t2', 
                   's078_t1', 's084_t1', 's090_t2', 's090_t3', 's094_t1', 
                   's094_t2', 's096_t1') 

# load and tidy parcel data
parcellations = read_csv('../data/fxParcellations.csv') %>%
  mutate(label = ifelse(parcellation %in% self_parcels, 'self',
                 ifelse(parcellation %in% social_parcels, 'social', 
                 ifelse(parcellation %in% control_yeo_parcels, 'other', 'remove')))) %>%
  filter(!label == "remove") %>%
  mutate(wave = paste0("t", as.numeric(c(`10` = 1, `13` = 2, `16` = 3)[as.character(age)]))) %>%
  select(-age) %>%
  unite(sub_wave, c(subjectID, wave), remove = FALSE) %>%
  group_by(parcellation) %>%
  mutate(inclusion = ifelse(sub_wave %in% mri_exclusions, "excluded from MRI", "completed MRI"),
         beta = ifelse(sub_wave %in% mri_exclusions, NA, beta),
         sd = ifelse(sub_wave %in% mri_exclusions, NA, sd),
         beta_std = scale(beta, center = FALSE, scale = TRUE),
         mean_beta_std = mean(beta_std, na.rm = TRUE)) %>%
    select(-sub_wave) %>%
  ungroup()

# exclude parameter estimates 3 SD from the mean
parcellations_ex = parcellations %>%
  mutate(beta_std = ifelse(beta_std > mean_beta_std + 3 | beta_std < mean_beta_std - 3, NA, beta_std))

load demographic data and merge

# demographics
demo = read.csv("../data/SFIC_age.pds.gender.csv") %>%
  rename("subjectID" = SID,
         "wave" = wavenum,
         "gender" = Gender) %>%
  mutate(subjectID = sprintf("s%03d", subjectID),
         wave = paste0("t", wave),
         age_c = age - 13,
         age_c2 = age_c^2,
         pdss_c = pdss - 3,
         pdss_c2 = pdss_c^2)

# merge data
merged = parcellations_ex %>%
  full_join(., demo, by = c("subjectID", "wave")) %>%
  mutate(inclusion = ifelse(is.na(inclusion), "didn't complete MRI", inclusion)) %>%
  filter(!(subjectID == "s086" & wave == "t3")) #no MRI, task, or self-report data was collected

# subset data for modeling
neuro_model_data = merged %>%
  filter(!is.na(beta)) %>%
  select(subjectID, wave, age, age_c, age_c2, target, domain, parcellation, label, beta, beta_std)

neuro_model_data_ex = neuro_model_data  %>%
  na.omit()

# dummy code target and domain
neuro_model_data_dummy = neuro_model_data_ex %>%
  mutate(target = ifelse(target == "self", .5, -.5),
         domain = ifelse(domain == "social", .5, -.5))

visualize raw

Visualize the developmental trajectories using the raw data

hypothesis 0

domain_parc_plot_raw = neuro_model_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, wave, label, domain, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = beta_std_avg, color = domain)) + 
  geom_smooth(aes(group = interaction(parcellation, domain), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(aes(fill = domain), method = "lm", formula = y ~ poly(x, 2), size = 1.5) +
  scale_color_manual(values = pal_social_academic) +
  scale_fill_manual(values = pal_social_academic) +
  scale_size_manual(values = c(.05, .1, .1)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = c(-1, 0, 1)) + 
  coord_cartesian(ylim = c(-1.2, 1.2)) +
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = '\nage', y = 'mean standardized parameter estimate\n', color = '', fill = '') +
  theme_minimal(base_size = 12) +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

target_parc_plot_raw = neuro_model_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, wave, label, target, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = beta_std_avg, color = target)) + 
  geom_smooth(aes(group = interaction(parcellation, target), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(aes(fill = target), method = "lm", formula = y ~ poly(x, 2), size = 1.5) +
  scale_color_manual(values = pal_self_other) +
  scale_fill_manual(values = pal_self_other) +
  scale_size_manual(values = c(.05, .1, .1)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = c(-1, 0, 1)) + 
  coord_cartesian(ylim = c(-1.2, 1.2)) +
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = '\nage', y = 'mean standardized parameter estimate\n', color = '', fill = '') +
  theme_minimal(base_size = 12) +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

(h0_raw = cowplot::plot_grid(domain_parc_plot_raw, target_parc_plot_raw,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 1

domain_plot_raw = neuro_model_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, wave, label, domain, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = beta_std_avg, group = domain, color = domain)) +
  geom_smooth(aes(fill = domain), method = 'lm', formula = y ~ poly(x,2), alpha = .2) + 
  scale_color_manual(values = pal_social_academic) +
  scale_fill_manual(values = pal_social_academic) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = round(seq(-.4, .6, .2), 1)) +
  coord_cartesian(ylim = c(-.45, .65)) +
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = '\nage', y = 'mean standardized parameter estimate\n', color = '', fill = '') +
  dcbw +
  theme(legend.position = "none")

soc_acad_plot_raw = neuro_model_data %>%
  group_by(subjectID, age, label, domain, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  select(subjectID, age, label, domain, beta_std_avg) %>%
  unique() %>%
  spread(domain, beta_std_avg) %>%
  mutate(avg_diff = social - academic) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = avg_diff)) +
  geom_smooth(aes(group = parcellation, size = label), method = "lm", formula = y ~ poly(x, 2),
              se = FALSE, color = "grey50") +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = TRUE, color = pal_social_academic[2], fill = pal_social_academic[2], size = 1.5) + 
  scale_size_manual(values = c(.1, .1, .1)) +
  scale_y_continuous(breaks = round(seq(-.4, .6, .2), 1)) +
  coord_cartesian(ylim = c(-.45, .65)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean standardized BOLD signal value\n",  color = '', fill = '') +
  dcbw +
  theme(legend.position = "none")

(h1_raw = cowplot::plot_grid(domain_plot_raw, soc_acad_plot_raw,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 2

target_plot_raw = neuro_model_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, wave, label, target, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = beta_std_avg, group = target, color = target)) +
  geom_smooth(aes(fill = target), method = 'lm', formula = y ~ poly(x,2), alpha = .2) + 
  scale_color_manual(values = pal_self_other) +
  scale_fill_manual(values = pal_self_other) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = round(seq(-.4, .4, .2), 1)) +
  coord_cartesian(ylim = c(-.4, .5)) +
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = '\nage', y = 'mean standardized parameter estimate\n', color = '', fill = '') +
  dcbw +
  theme(legend.position = "none")

self_other_plot_raw = neuro_model_data %>%
  group_by(subjectID, age, label, target, parcellation) %>%
  mutate(beta_std_avf = mean(beta_std, na.rm = TRUE)) %>%
  select(subjectID, age, label, target, beta_std_avf) %>%
  unique() %>%
  spread(target, beta_std_avf) %>%
  mutate(avg_diff = self - other) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = avg_diff)) +
  geom_smooth(aes(group = parcellation, size = label), method = "lm", formula = y ~ poly(x, 2),
              se = FALSE, color = "grey50") +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = TRUE, color = pal_self_other[2], fill = pal_self_other[2], size = 1.5) + 
  scale_size_manual(values = c(.1, .1, .1)) +
  scale_y_continuous(breaks = round(seq(-.4, .4, .2), 1)) +
  coord_cartesian(ylim = c(-.4, .5)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean standardized BOLD signal value\n",  color = '', fill = '') +
  dcbw +
  theme(legend.position = "none")

(h2_raw = cowplot::plot_grid(target_plot_raw, self_other_plot_raw,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 3

int_plot_raw = neuro_model_data %>%
  distinct(parcellation, target, domain, age, beta_std, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = beta_std, group = interaction(target, domain), color = domain, linetype = target)) +
  geom_smooth(aes(fill = domain), method = 'lm', formula = y ~ poly(x,2), alpha = .2) + 
  scale_y_continuous(breaks = c(-.4, -.2, 0, .2, .4)) +
  coord_cartesian(ylim = c(-.4, .45)) +
  scale_color_manual(values = pal_social_academic) +
  scale_fill_manual(values = pal_social_academic) +
  scale_linetype_manual(name = "", values = c("dotted", "solid")) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = '\nage', y = 'mean standardized parameter estimate\n', color = '', linetype = '', fill = '') +
  guides(linetype = guide_legend(override.aes = list(color = "black"))) +
  dcbw +
  theme(legend.position = c(.75, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(.5, "cm"),
        legend.direction = "vertical",
        legend.box = "horizontal")

soc_acad_int_plot_raw = neuro_model_data %>%
  group_by(subjectID, age, label, target, domain, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  select(subjectID, age, label, target, domain, beta_std_avg) %>%
  unique() %>%
  spread(domain, beta_std_avg) %>%
  mutate(avg_diff = social - academic) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = avg_diff, color = target)) +
  geom_smooth(aes(group = interaction(parcellation, target), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(aes(fill = target), method = 'lm', formula = y ~ poly(x,2), 
              se = TRUE, size = 1.5) + 
  scale_color_manual(values = pal_self_other) +
  scale_fill_manual(values = pal_self_other) +
  scale_size_manual(values = c(.1, .1, .1)) +
  scale_y_continuous(breaks = round(seq(-.6, .6, .2), 1)) +
  coord_cartesian(ylim = c(-.65, .65)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean standardized BOLD signal value\n",  color = '', fill = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

self_other_int_plot_raw = neuro_model_data %>%
  group_by(subjectID, age, label, domain, target, parcellation) %>%
  mutate(beta_std_avg = mean(beta_std, na.rm = TRUE)) %>%
  select(subjectID, age, label, domain, target, beta_std_avg) %>%
  unique() %>%
  spread(target, beta_std_avg) %>%
  mutate(avg_diff = self - other) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = avg_diff, color = domain)) +
  geom_smooth(aes(group = interaction(parcellation, domain), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(aes(fill = domain), method = 'lm', formula = y ~ poly(x,2), 
              se = TRUE, size = 1.5) + 
  scale_color_manual(values = pal_social_academic) +
  scale_fill_manual(values = pal_social_academic) +
  scale_size_manual(values = c(.1, .1, .1)) +
  scale_y_continuous(breaks = round(seq(-.6, .6, .2), 1)) +
  coord_cartesian(ylim = c(-.65, .65)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean standardized BOLD signal value\n",  color = '', fill = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

(h3_raw = cowplot::plot_grid(int_plot_raw, soc_acad_int_plot_raw, self_other_int_plot_raw,
                       labels = c('A', 'B', 'C'), ncol = 3))

run models

Estimate full models

domain x age

# specify model
model_domain_equation = formula(beta_std ~ 1 + age_c*domain*label +
                                  age_c2*domain*label +
                                  (1 + age_c*domain | subjectID) +
                                  (1 + age_c*domain + age_c2*domain | parcellation))

model_domain_formula = lFormula(model_domain_equation, data = neuro_model_data_dummy)

# calculate max number of iterations
model_domain_numFx = length(dimnames(model_domain_formula$X)[[2]])

model_domain_numRx = sum(as.numeric(lapply(model_domain_formula$reTrms$cnms, function(x) {
  l <- length(x)
  (l*(l - 1)) / 2 + l
})))

model_domain_maxfun = 10*(model_domain_numFx + model_domain_numRx + 1)^2

# run or load the model
if (file.exists("../data/model_domain_yeo.RDS")) {
  model_domain = readRDS("../data/model_domain_yeo.RDS")
} else {
  model_domain = lmer(model_domain_equation, data = neuro_model_data_dummy, REML = F, #Use ML since we want to compare random effects
                      verbose = 2,
                      control = lmerControl(optCtrl = list(maxfun = model_domain_maxfun), optimizer = "bobyqa", calc.derivs = FALSE))
  saveRDS(model_domain, "../data/model_domain_yeo.RDS")
}

domain x target x age

# specify model
model_target_equation = formula(beta_std ~ 1 + age_c*target*domain*label +
                                  age_c2*target*domain*label +
                                  (1 + age_c*target*domain | subjectID) +
                                  (1 + age_c*target*domain + age_c2*target*domain | parcellation))

# calculate max number of iterations
model_target_formula = lFormula(model_target_equation, data = neuro_model_data_dummy)

model_target_numFx = length(dimnames(model_target_formula$X)[[2]])

model_target_numRx = sum(as.numeric(lapply(model_target_formula$reTrms$cnms, function(x) {
  l <- length(x)
  (l*(l - 1)) / 2 + l
})))

model_target_maxfun = 10*(model_target_numFx + model_target_numRx + 1)^2

# run or load the model
if (file.exists("../data/model_target_yeo.RDS")) {
  model_target = readRDS("../data/model_target_yeo.RDS")
} else {
  model_target = lmer(model_target_equation, data = neuro_model_data_dummy, REML = F, #Use ML since we want to compare random effects
                      verbose = 2,
                      control = lmerControl(optCtrl = list(maxfun = model_target_maxfun), optimizer = "bobyqa", calc.derivs = FALSE))
  saveRDS(model_target, "../data/model_target_yeo.RDS")
}

compare models

anova(model_domain, model_target) %>%
  as.data.frame() %>%
  select(npar, AIC, Chisq, Df, `Pr(>Chisq)`) %>%
  rownames_to_column() %>%
  rename("model" = rowname,
         "model df" = Df,
         "X2" = Chisq,
         "X2 df" = Df,
         "p" = `Pr(>Chisq)`) %>%
  mutate(AIC = round(AIC, 2),
         X2 = round(X2, 2),
         p = round(p, 3),
         p = ifelse(p == 0, "< .001", gsub("0.(.*)", ".\\1", p))) %>%
  mutate_if(is.numeric, funs(ifelse(is.na(.), "--", .))) %>%
  mutate_if(is.character, funs(ifelse(is.na(.), "--", .))) %>%
  kable(format = "pandoc")
model npar AIC X2 X2 df p
model_domain 50 179086.6 – – –
model_target 151 178695.2 593.34 101 < .001

summarize best fitting model

model_target %>%
  broom.mixed::tidy(effects = c("ran_pars", "fixed"), conf.int = TRUE) %>%
  filter(effect == "fixed") %>%
  select(-group) %>%
  rename("b" = estimate,
         "SE" = std.error,
         "t" = statistic,
         "p" = p.value) %>%
  mutate(p = round(p, 3),
         p = ifelse(p == 0, "< .001", gsub("0.(.*)", ".\\1", sprintf("%.3f", p))),
         term = gsub("\\(Intercept\\)", "Intercept (age 13, label (control))", term),
         term = gsub("target", "Target", term),
         term = gsub("domain", "Domain", term),
         term = gsub("labelself", "Label (self)", term),
         term = gsub("labelsocial", "Label (social)", term),
         term = gsub("age_c", "Age", term),
         term = gsub(":", " x ", term),
         term = gsub("sd__", "", term),
         term = gsub("Observation", "observation", term),
         effect = gsub("ran_pars", "random", effect),
         `b [95% CI]` = ifelse(effect == "fixed", sprintf("%.3f [%.3f, %.3f]", b, conf.low, conf.high), "--")) %>%
  mutate_if(is.numeric, round, 3) %>%
  mutate_if(is.numeric, funs(ifelse(is.na(.), "--", .))) %>%
  mutate_if(is.character, funs(ifelse(is.na(.), "--", .))) %>%
  select(term, `b [95% CI]`, SE, t, df, p) %>%
  kable(format = "pandoc")
term b [95% CI] SE t df p
Intercept (age 13, label (control)) -0.089 [-0.206, 0.028] 0.060 -1.488 154.354 .139
Age -0.009 [-0.023, 0.005] 0.007 -1.274 110.500 .205
Target 0.085 [0.051, 0.118] 0.017 4.963 185.756 < .001
Domain -0.030 [-0.074, 0.013] 0.022 -1.366 160.579 .174
Label (self) 0.054 [-0.203, 0.310] 0.131 0.411 140.035 .682
Label (social) 0.330 [0.140, 0.519] 0.097 3.411 140.021 .001
Age2 0.006 [0.004, 0.008] 0.001 5.174 275.060 < .001
Age x Target 0.008 [-0.001, 0.018] 0.005 1.679 83.733 .097
Age x Domain -0.016 [-0.030, -0.002] 0.007 -2.270 73.731 .026
Target x Domain -0.043 [-0.107, 0.021] 0.033 -1.305 230.162 .193
Age x Label (self) 0.020 [0.002, 0.039] 0.009 2.193 139.855 .030
Age x Label (social) 0.032 [0.018, 0.045] 0.007 4.637 139.721 < .001
Target x Label (self) 0.180 [0.118, 0.242] 0.032 5.694 304.359 < .001
Target x Label (social) -0.126 [-0.171, -0.080] 0.023 -5.406 302.249 < .001
Domain x Label (self) 0.095 [0.021, 0.169] 0.038 2.531 186.386 .012
Domain x Label (social) 0.147 [0.093, 0.202] 0.028 5.321 185.475 < .001
Target x Age2 -0.010 [-0.013, -0.006] 0.002 -5.439 994.816 < .001
Domain x Age2 -0.004 [-0.008, -0.001] 0.002 -2.297 1727.074 .022
Label (self) x Age2 -0.004 [-0.008, 0.000] 0.002 -1.853 145.026 .066
Label (social) x Age2 -0.010 [-0.013, -0.006] 0.002 -6.028 144.205 < .001
Age x Target x Domain -0.001 [-0.021, 0.020] 0.010 -0.058 72.710 .954
Age x Target x Label (self) 0.021 [0.007, 0.035] 0.007 2.972 1628.706 .003
Age x Target x Label (social) 0.001 [-0.009, 0.011] 0.005 0.245 1617.949 .806
Age x Domain x Label (self) 0.006 [-0.009, 0.021] 0.008 0.759 375.885 .448
Age x Domain x Label (social) 0.007 [-0.004, 0.019] 0.006 1.323 373.762 .187
Target x Domain x Label (self) 0.020 [-0.098, 0.137] 0.060 0.327 1108.322 .743
Target x Domain x Label (social) 0.140 [0.053, 0.226] 0.044 3.170 1099.879 .002
Target x Domain x Age2 0.003 [-0.004, 0.010] 0.004 0.794 2534.928 .427
Target x Label (self) x Age2 -0.003 [-0.011, 0.004] 0.004 -0.955 1003.759 .340
Target x Label (social) x Age2 0.012 [0.006, 0.017] 0.003 4.314 995.825 < .001
Domain x Label (self) x Age2 0.010 [0.003, 0.017] 0.004 2.828 1581.638 .005
Domain x Label (social) x Age2 0.009 [0.004, 0.014] 0.003 3.482 1568.709 .001
Age x Target x Domain x Label (self) -0.001 [-0.028, 0.026] 0.014 -0.082 2761.297 .935
Age x Target x Domain x Label (social) -0.009 [-0.029, 0.011] 0.010 -0.866 2742.990 .387
Target x Domain x Label (self) x Age2 0.004 [-0.010, 0.017] 0.007 0.507 8601.358 .612
Target x Domain x Label (social) x Age2 -0.006 [-0.016, 0.004] 0.005 -1.199 8535.720 .231
# summarize random effects
print(VarCorr(model_target), comp = c("Variance","Std.Dev."), digits = 3)
##  Groups       Name                 Variance   Std.Dev. Corr                   
##  parcellation (Intercept)          0.25816928 0.50810                         
##               age_c                0.00114872 0.03389   0.55                  
##               target               0.00229747 0.04793  -0.35 -0.23            
##               domain               0.00863484 0.09292   0.07 -0.07 -0.46      
##               age_c2               0.00002430 0.00493  -0.50 -0.51  0.39 -0.43
##               age_c:target         0.00002846 0.00533  -0.10  0.63  0.45 -0.11
##               age_c:domain         0.00017380 0.01318   0.78  0.84  0.02 -0.32
##               target:domain        0.00288012 0.05367   0.03  0.23 -0.09  0.82
##               target:age_c2        0.00001420 0.00377   0.94  0.61 -0.55  0.35
##               domain:age_c2        0.00000568 0.00238   0.54  0.40  0.21  0.21
##               age_c:target:domain  0.00006673 0.00817  -0.25 -0.31  0.54 -0.92
##               target:domain:age_c2 0.00000504 0.00225  -0.90 -0.56  0.15 -0.15
##  subjectID    (Intercept)          0.00961680 0.09807                         
##               age_c                0.00164330 0.04054   0.30                  
##               target               0.00500456 0.07074  -0.19  0.29            
##               domain               0.01167156 0.10804   0.08  0.21  0.14      
##               age_c:target         0.00071070 0.02666   0.04 -0.19 -0.13 -0.41
##               age_c:domain         0.00215177 0.04639  -0.13  0.26  0.30 -0.12
##               target:domain        0.01877223 0.13701   0.14 -0.10  0.29 -0.28
##               age_c:target:domain  0.00370953 0.06091   0.14 -0.02 -0.09  0.02
##  Residual                          0.61199067 0.78230                         
##                                           
##                                           
##                                           
##                                           
##                                           
##                                           
##  -0.27                                    
##  -0.45  0.46                              
##  -0.62  0.45 -0.02                        
##  -0.69 -0.04  0.68  0.30                  
##  -0.76  0.46  0.60  0.57  0.57            
##   0.64 -0.14  0.00 -0.87 -0.55 -0.36      
##   0.73 -0.17 -0.79 -0.31 -0.89 -0.85  0.35
##                                           
##                                           
##                                           
##                                           
##                                           
##   0.23                                    
##   0.55  0.45                              
##   0.30 -0.10  0.04                        
## 

simple slopes

Estimate simple slopes to test interactions at specific levels

run models

# self social > academic
self_social = emmeans::emtrends(model_target, pairwise ~ domain,
                  var = "age_c", at = list(target =.5, label="social"),
                  lmerTest.limit = 188577)$contrasts %>%
  data.frame() %>%
  mutate(contrast = "self social > academic",
         parcel = "social",
         age_effect = "linear") %>%
  bind_rows(emmeans::emtrends(model_target, pairwise ~ domain,
                  var = "age_c2", at = list(target =.5, label="social"),
                  lmerTest.limit = 188577)$contrasts %>%
  data.frame() %>%
  mutate(contrast = "self social > academic",
         parcel = "social",
         age_effect = "quadratic"))

# social self > other
social_self = emmeans::emtrends(model_target, pairwise ~ target,
                  var = "age_c", at = list(domain =.5, label="self"),
                  lmerTest.limit = 188577)$contrasts %>%
  data.frame() %>%
  mutate(contrast = "social self > other",
         parcel = "self",
         age_effect = "linear") %>%
  bind_rows(emmeans::emtrends(model_target, pairwise ~ target,
                  var = "age_c2", at = list(domain =.5, label="self"),
                  lmerTest.limit = 188577)$contrasts %>%
  data.frame() %>%
  mutate(contrast = "social self > other",
         parcel = "self",
         age_effect = "quadratic"))

make table

social_self %>%
  bind_rows(self_social) %>%
  select(contrast, parcel, age_effect, estimate, SE, df, t.ratio, p.value) %>%
  rename("b" = estimate,
         "t" = t.ratio,
         "p" = p.value) %>%
  mutate(b = round(b, 3) * -1, #flip signs for it's .5 - (-.5)
         SE = round(SE, 3),
         df = round(df, 2),
         t = abs(round(t, 2)),
         p = round(p, 3)) %>%
  kable(format = "pandoc")
contrast parcel age_effect b SE df t p
social self > other self linear 0.028 0.011 275.32 2.57 0.011
social self > other self quadratic -0.010 0.005 3314.59 2.14 0.033
self social > academic social linear -0.013 0.009 88.56 1.42 0.159
self social > academic social quadratic 0.003 0.003 4486.70 0.99 0.324

visualize fitted models

Visualize the developmental trajectory using the fitted values from the domain x target x age model

reForm = as.formula("~(1 + age_c*target*domain + age_c2*target*domain | parcellation)")

neuro_plot_data = with(neuro_model_data_dummy, 
                    expand.grid(target = unique(target), 
                                domain = unique(domain),
                                parcellation = unique(parcellation),
                                age = unique(age),
                                stringsAsFactors = F)) %>%
  mutate(label = ifelse(parcellation %in% self_parcels, 'self',
                 ifelse(parcellation %in% social_parcels, 'social', 'other')),
         age_c = age - 13, 
         age_c2 = age_c^2, 
         subjectID = NA)
neuro_plot_data$expected = predict(model_target, newdata = neuro_plot_data, re.form = reForm)
neuro_plot_data$expected_mean = predict(model_target, newdata = neuro_plot_data, re.form = NA)

neuro_plot_data = neuro_plot_data %>%
  mutate(target = factor(target, levels = c(-.5, .5), labels = c("other", "self")),
         domain = factor(domain, levels = c(-.5, .5), labels = c("academic", "social")))

hypothesis 0

domain_parc_plot = neuro_plot_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, age, label, domain, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = expected_avg, color = domain)) + 
  geom_smooth(aes(group = interaction(parcellation, domain), size = label), method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(method = "lm", formula = y ~ poly(x, 2), size = 1.25, se = FALSE) +
  scale_color_manual(name = "", values = pal_social_academic) +
  scale_size_manual(values = c(.05, .1, .1)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = c(-1, 0, 1)) + 
  coord_cartesian(ylim = c(-1.2, 1.2)) +
  facet_grid(~label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n") + 
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

target_parc_plot = neuro_plot_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  group_by(subjectID, age, label, target, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  ggplot(aes(x = age, y = expected_avg, color = target)) + 
  geom_smooth(aes(group = interaction(parcellation, target), size = label), method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(method = "lm", formula = y ~ poly(x, 2), size = 1.25, se = FALSE) +
  scale_color_manual(values = pal_self_other) +
  scale_size_manual(values = c(.05, .1, .1)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  scale_y_continuous(breaks = c(-1, 0, 1)) + 
  coord_cartesian(ylim = c(-1.2, 1.2)) +
  facet_grid(~label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n", color = "") + 
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

(h0_fitted = cowplot::plot_grid(domain_parc_plot, target_parc_plot,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 1

domain_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, domain, parcellation) %>%
  mutate(expected_avg = mean(expected_mean, na.rm = TRUE)) %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_avg, color = domain)) +
  geom_rect(data = subset(label_df, label == "social"), aes(fill = label), color = NA, alpha = .07,
            xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, show.legend = FALSE) +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), alpha = .2, se = FALSE, size = 1.25) + 
  scale_y_continuous(breaks = seq(-.2, .45, .2)) +
  coord_cartesian(ylim = c(-.3, .45)) +
  scale_color_manual(values = pal_social_academic) +
  scale_fill_manual(values = "lightgrey") +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

soc_acad_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, domain, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  select(subjectID, age, label, domain, expected_avg) %>%
  unique() %>%
  spread(domain, expected_avg) %>%
  mutate(expected_diff = social - academic) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_diff)) +
  geom_rect(data = subset(label_df, label == "social"), aes(fill = label), alpha = .07,
            xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, show.legend = FALSE) +
  geom_smooth(aes(group = parcellation, size = label), method = "lm", formula = y ~ poly(x, 2),
              se = FALSE, color = "grey50") +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = FALSE, color = pal_social_academic[2], size = 1.5) + 
  scale_fill_manual(values = "lightgrey") +
  scale_size_manual(values = c(.03, .1, .1)) +
  scale_y_continuous(breaks = seq(-.2, .45, .2)) +
  coord_cartesian(ylim = c(-.3, .45)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = "none")

(h1_fitted = cowplot::plot_grid(domain_plot, soc_acad_plot,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 2

target_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, target, parcellation) %>%
  mutate(expected_avg = mean(expected_mean, na.rm = TRUE)) %>%
  distinct(parcellation, target, target, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_avg, color = target)) +
  geom_rect(data = subset(label_df, label == "self"), aes(fill = label), color = NA, alpha = .07,
            xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, show.legend = FALSE) +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), alpha = .2, se = FALSE, size = 1.25) + 
  scale_y_continuous(breaks = seq(-.2, .3, .1)) +
  coord_cartesian(ylim = c(-.2, .35)) +
  scale_color_manual(values = pal_self_other) +
  scale_fill_manual(values = "lightgrey") +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

self_other_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, target, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  select(subjectID, age, label, target, expected_avg) %>%
  unique() %>%
  spread(target, expected_avg) %>%
  mutate(expected_diff = self - other) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_diff)) +
  geom_rect(data = subset(label_df, label == "self"), aes(fill = label), alpha = .07,
            xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, show.legend = FALSE) +
  geom_smooth(aes(group = parcellation, size = label), method = "lm", formula = y ~ poly(x, 2),
              se = FALSE, color = "grey50") +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = FALSE, color = pal_self_other[2], size = 1.5) + 
  scale_fill_manual(values = "lightgrey") +
  scale_size_manual(values = c(.03, .1, .1)) +
  scale_y_continuous(breaks = seq(-.2, .3, .1)) +
  coord_cartesian(ylim = c(-.2, .35)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = "none")

(h2_fitted = cowplot::plot_grid(target_plot, self_other_plot,
                                labels = c('A', 'B'), ncol = 2,
                                rel_widths = c(1, 1)))

hypothesis 3

int_plot = neuro_plot_data %>%
  distinct(parcellation, target, domain, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_mean, group = interaction(target, domain), color = domain, linetype = target)) +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), alpha = 1, se = FALSE, size = 1.25) + 
  scale_y_continuous(breaks = c(-.4, -.2, 0, .2, .4)) +
  coord_cartesian(ylim = c(-.4, .55)) +
  scale_color_manual(values = pal_social_academic) +
  scale_linetype_manual(name = "", values = c("dotted", "solid")) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '', linetype = '') +
  guides(linetype = guide_legend(override.aes = list(color = "black"))) +
  dcbw +
  theme(legend.position = c(.75, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(.5, "cm"),
        legend.direction = "vertical",
        legend.box = "horizontal")

soc_acad_int_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, target, domain, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  select(subjectID, age, label, target, domain, expected_avg) %>%
  unique() %>%
  spread(domain, expected_avg) %>%
  mutate(expected_diff = social - academic) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_diff, color = target)) +
  geom_smooth(aes(group = interaction(parcellation, target), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = FALSE, size = 1.5) + 
  scale_color_manual(values = pal_self_other) +
  scale_size_manual(values = c(.03, .1, .1)) +
  scale_y_continuous(breaks = c(-.4, -.2, 0, .2, .4)) +
  coord_cartesian(ylim = c(-.4, .55)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))


self_other_int_plot = neuro_plot_data %>%
  group_by(subjectID, age, label, domain, target, parcellation) %>%
  mutate(expected_avg = mean(expected, na.rm = TRUE)) %>%
  select(subjectID, age, label, domain, target, expected_avg) %>%
  unique() %>%
  spread(target, expected_avg) %>%
  mutate(expected_diff = self - other) %>%
  distinct(parcellation, age, label, .keep_all = T) %>%
  ggplot(aes(x = age, y = expected_diff, color = domain)) +
  geom_smooth(aes(group = interaction(parcellation, domain), size = label),
              method = "lm", formula = y ~ poly(x, 2), se = FALSE, show.legend = FALSE) +
  geom_smooth(method = 'lm', formula = y ~ poly(x,2), 
              se = FALSE, size = 1.5) + 
  scale_color_manual(values = pal_social_academic) +
  scale_size_manual(values = c(.03, .1, .1)) +
  scale_y_continuous(breaks = c(-.4, -.2, 0, .2, .4)) +
  coord_cartesian(ylim = c(-.4, .55)) +
  scale_x_continuous(breaks = c(10, 13, 16)) + 
  facet_grid(~ label, labeller = parcel_labeller) +
  labs(x = "\nage", y = "mean predicted BOLD signal value\n",  color = '') +
  dcbw +
  theme(legend.position = c(.85, .15),
        legend.spacing.y = unit(.01, 'cm'),
        legend.margin = unit(0, "cm"))

(h3_fitted = cowplot::plot_grid(int_plot, soc_acad_int_plot, self_other_int_plot,
                       labels = c('A', 'B', 'C'), ncol = 3))

compare raw and fitted models

hypothesis 0

raw

h0_raw

fitted

h0_fitted

hypothesis 1

raw

h1_raw

fitted

h1_fitted

hypothesis 2

raw

h2_raw

fitted

h2_fitted

hypothesis 3

raw

h3_raw

fitted

h3_fitted